The energy limitation remains one of the biggest constraints in drone path planning, since it prevents drones from performing long surveillance missions. To assist drones in such missions, recharging stations have recently been introduced. They are platforms where the drone can autonomously land to recharge its battery before continuing the mission. However, the cost of those platforms remains a significant obstacle to their adoption. Consequently, it is important to reduce their number while planning the path of the drone. This work introduces the Single Drone Multiple Recharging Stations on Large Farm problem (SD-MRS-LF). A large farm is considered as an area of interest to cover with a set of candidate locations where recharging stations can be installed. The aim is to determine the path of the drone that minimizes the number of locations for recharging stations as well as the completion time of the surveillance mission. This path planning problem falls within the realm of computational geometry and is related to similar problems that are encountered in the field of robotics. The problem is complicated due to environmental constraints on farms such as wind speed and direction, which produce asymmetries in the optimal solution. A back-and-forth-k-opt simulated annealing (BFKSA) approach is proposed to solve the defined problem. The new approach is compared to the basic back-and-forth (BF) and a K-opt variant of the well-known simulated annealing (KSA) approach over a set of 20 random topologies in different environmental conditions. The results from computational experiments show that the BFKSA approach outperforms the others, in terms of providing feasible solutions and minimizing the number of recharges.

Multilevel inverter has appeared as one of the important topologies in the area of high power and medium voltage because it can efficiently realize lower harmonics with reduced switching frequency. These Multilevel inverters (MLI) improve the energy quality shaped by producing many voltage levels. However, improving the quality of the output voltage of a multilevel inverter requires many switches, which tend to weigh down the structure and make it complex to control. This work deals with a comparison in terms of the spectral content of two configurations of thirty-one-level inverters for injection into the electrical grid. The first configuration is a classical cascaded H-bridge and the second one is a reconfigured Packed U-Cell (PUC) multilevel inverter. The classical configuration requires sixteen switches while the second uses only ten ones. The control technique based on the half-height modulation was performed and the Total Harmonic Distortion (THD) is calculated for each topology. For the PUC, we got a THD equal to 2.61% while we got 2.72% for the cascaded H-bridge. These results obtained in the MATLAB/Simulink environment, show that the reconfigured structure of the PUC inverter is a good candidate for injection into the electrical network.

The Vehicle Routing Problem consists in finding a routing plan for vehicles of identical capacity to satisfy the demands of a set of customers. Time window constraints mean that customers can only be served within a pre-defined time window. Researchers have intensively studied this problem because of its wide range of applications in logistics. In this paper, we tackle the problem on an economical point of view with a focus on capital expenditure (CAPEX), where the minimization of the number of vehicles is more important than the total traveling distance. This customization finds its applications in scenarios with limited CAPEX or seasonal/temporary operations. In these cases, the CAPEX should be minimized as much as possible to reduce the overall cost of the operation, while satisfying time window constraints. We provide an Ant Colony Optimization-based Tabu List (ACOTL). We test the proposed approach on the well-known Solomon's benchmarks. We compare experiments results to Dynamic Programming on small size instances and later to the best-known results in the literature on large size instances. ACOTL allows to reduce the number of vehicles used sometimes up to three units, compared to the best-known results, especially for instances where customers are geographically in clusters randomly distributed with vehicles of low or medium charges.

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